Network Cosmology

Dmitri Krioukov,1Maksim Kitsak,1Robert S. Sinkovits,2David Rideout,3David Meyer,3and Marian Bogu na41Cooperative Association for Internet Data Analysis (CAIDA),University of California, San Diego (UCSD), La Jolla, CA 92093, USA2San Diego Supercomputer Center (SDSC), University of California, San Diego (UCSD), La Jolla, CA 92093, USA3Department of Mathematics, University of California, San Diego (UCSD), La Jolla, CA 92093, USA4Departament de Fsica Fonamental, Universitat de Barcelona, Mart i Franqu`es 1, 08028 Barcelona, SpainCausal sets are an approach to quantum gravity in which the causal structure of spacetime playsa fundamental role. The causal set is a quantum network which underlies the fabric of spacetime.The nodes in this network are tiny quanta of spacetime, with two such quanta connected if theyare causally related. Here we show that the structure of these networks in de Sitter spacetime,such as our accelerating universe, is remarkably similar to the structure of complex networksthebrain or the Internet, for example. Specically, we show that the node degree distribution of causalsets in de Sitter spacetime is described by a power law with exponent 2, similar to many complexnetworks. Quantifying the dierences between the causal set structure in de Sitter spacetime and inthe real universe, we nd that since the universe today is relatively young, its power-law exponentis not 2 but 3/4, yet exponent 2 is currently emerging. Finally, we show that as a consequence ofa simple geometric duality, the growth dynamics of complex networks and de Sitter causal sets areasymptotically identical. These ndings suggest that unexpectedly similar mechanisms may shapethe large-scale structure and dynamics of complex systems as dierent as the brain, the Internet,and the universe.The nite speed of light c is a fundamental constant of our physical world, responsible for the non-trivial causalstructure of the universe [1]. If in some coordinate system the spatial distance x between two spacetime events (pointsin space and time) is larger than ct, where t is the time dierence between them, then these two events cannot becausally related since no signal can propagate faster than c (Fig. 1(a)). Causality is fundamental not only in physics,but also in elds as disparate as distributed systems [2, 3], criminology [4], and philosophy [5].Causal sets [6] are the main building block in an approach to quantum gravity, motivated by mathematical resultsstating that the structure of a relativistic spacetime is almost fully determined by its causal structure alone [79],suggesting that this structure is fundamental. At a fundamentally tiniest scale (the Planck scale, lP 1035metersand tP 1043seconds), one expects spacetime not to be continuous but to have a discrete structure [10], similarto ordinary matter, which is not continuous at atomic scales but instead is composed of discrete atoms. The causalset approach postulates that spacetime at the Planck scale is a discrete causal set, or causet. A causet is a set ofelements (Planck-scale atoms of spacetime) endowed with causal relationships among them. A causet is thus anetwork in which nodes are spacetime quanta, and links are causal relationships between them. To make contact withGeneral Relativity, one expects the theory to give rise to causal sets which are constructed by a Poisson process, i.e.space xtime t t=0 x=0 x=x0t=x0/cPs pastlight coneLs futurelight coneLPt=t0(a) LP(b)FIG. 1: Finite speed of light c, and causal structure of spacetime. In panel (a), a light source located at spatial coordinatex = 0 is switched on at time t = 0. This event, denoted by L in the gure, is not immediately visible to an observer located atdistance x0 from the light source. The observer does not see any light until time t = x0/c. Since no signal can propagate fasterthan c, the events on the observers world line, shown by the vertical dashed line, are not causally related to L until the worldline enters the Ls future light cone (yellow color) at t = x0/c. This light cone depicts the set of events that L can causallyinuence. An example is event P located on the observers world line x = x0 at time t = t0 > x0/c. The past light cone of P(green color) is the set of events that can causally inuence P. Events L and P lie within each others light cones. Panel (b)shows a set of points sprinkled into the considered spacetime patch. The red and green links show all causal connections ofevents L and P in the resulting causet. These links form a subset of all the links in the causet (not shown).arXiv:1203.2109v1

[gr-qc]

9

Mar

20122by sprinkling points into spacetime uniformly at random, and then connecting each pair of points i they lie withineach others light cones (Fig. 1(b)). According to the theorem in [11], causets constructed by Poisson sprinkling arerelativistically invariant, as opposed to regular lattices, for example. Therefore we will use Poisson sprinkling hereto construct causets corresponding to spacetimes. One of the goals in the causal set approach to quantum gravity,which we discuss no further here, is to identify causet growth laws based on fundamental physical principles whichcan grow causets similar to those obtained by Poisson sprinkling onto the spacetime of our universe [12, 13].In 1998 the expansion of our universe was found to be accelerating [14, 15]. Positive vacuum energy, or darkenergy, corresponding to a positive cosmological constant in the Einstein equations (Section II), is currently themost plausible explanation for this acceleration. Positive implies that the universe is asymptotically (at late times)described by de Sitter spacetime [16, 17]. We rst consider the structure of causets sprinkled onto de Sitter spacetime,and then quantify how dierent this structure is in the real universe.De Sitter spacetime is the solution of Einsteins eld equations for an empty universe with positive . The 1 + 1-dimensional de Sitter spacetime (the rst 1 stands for the space dimension; the second 1for time) can be visualizedas a one-sheeted 2-dimensional hyperboloid embedded in a at 3-dimensional Minkowski space (Fig. 2(a)). The lengthof horizontal circles in Fig. 2(a), corresponding to the volume of space at a moment of time, grows exponentially withtime t. Since causet nodes are distributed uniformly over spacetime, their number also grows exponentially with time.Specically, the number of nodes in a patch of d + 1-dimensional de Sitter spacetime between t = 0 and t = t0 (thecurrent time) scales as ed(tt0)/a, where a =_3/ is the inverse square root of the spacetime curvature (Section II).A node born at time t is connected to all nodes lying within its future light cone up to current time t0. Since the nodedensity is uniform, the expected number of such nodes and consequently the number of future connections emanatingfrom the node born at time t are proportional to the volume of this light cone, which scales as ed(t0t)/a(Section II).We thus have a combination of two exponentials, number of nodes etborn at time t and their degrees et,where = = d/a. This combination yields a power-law distribution P(k) k

of node degrees k in the causet,

where exponent = / + 1 = 2.In many complex networks the degree distribution is also a power law with close to 2 [19, 20]. In Fig. 3 we showtwo examples, the brain and the Internet, and juxtapose them against a de Sitter causet. In all the three networks,the exponent 2.Is there a connection revealing a mechanism responsible for the emergence of this structural similarity, going beyondthe degree distribution, and showing that other structural and dynamical properties of complex networks and de Sittercausets are similar? Remarkably, the answer is yes. To see this, consider a new spacetime quantum P that has justbeen born at current time t = t0 in Fig. 2. Upon its birth, P causally connects to all nodes in its past light cone. Asillustrated in Fig. 2, we map this past light cone to the upper sheet of the dual two-sheeted inner hyperboloid, whichis the standard hyperboloid representation of the hyperbolic plane [23]. This mapping sends Ps past light cone inthe de Sitter spacetime to a hyperbolic disc of radius t0 centered at Ps image in the hyperbolic space. Thereforeupon this mapping, the causal network becomes asymptotically identical to the complex networks in the model [18]shown to accurately describe the large-scale structure and growth dynamics of some complex networks, such as theInternet, metabolic networks, etc. Indeed, in that model, angular (spatial) distances in the hyperbolic space abstractintrinsic similarity distances between nodes in the network, while the radial (time) coordinate of a node is a functionof its birth time. According to the model, new node P is born at a random location on the edge of an expandinghyperbolic disc of radius r0, and connects to all existing nodes that lie within the hyperbolic disc of the same radius r0but centered at P. If we identify r0 t0, then this disc is asymptotically identical to Ps past light cone (Fig. 2). Allfurther details are given in Section II, where we show that the de Sitter causet is a particular case of [18] with = 2and strongest possible clustering, i.e. with the largest possible number of triangular subgraphs. Strong clustering isanother structural property often observed in complex networks [19, 20]. Considering causet growth as the birth ofnew nodes as the current time boundary t = t0 moves forward one causet element at a time, we conclude that theestablished equivalence implies that not only the structure, but also the growth dynamics of complex networks andde Sitter causets are asymptotically identical.The real universe is only asymptotically de Sitter [16, 17]. Due to non-zero matter density, the spacetime structureof the universe deviates from the structure of de Sitter spacetime. In fact, the matter and dark energy densities areof the same order of magnitude in the universe now, a coincidence known as a why now? puzzle in cosmology [2426]. This coincidence implies that the current rescaled cosmological time 0 = t0/a is approximately 1, meaningthat the universe is relatively young today. It transitions from the past matter-dominated era ( < 1) to the futuredark-energy-dominated era ( > 1). Since universe evolution has been dominated not by dark energy, but by matter,the degree distribution in the universe causet is a power law with exponent = 3/4, instead of = 2 (Fig. 4(a)).Yet, since dark energy begins to dominate, the = 2 scaling is currently emerging. Figure 4(b) traces the evolutionof the degree distribution in the universe in its past and future. According to the gure, in the matter-dominatedera with < 1, the degree distribution is a power law with exponent 3/4 up to a soft cut-o that grows with time.Above this soft cut-o, the distribution decays sharply. Once we reach times 1, e.g. today, we enter the dark-3deSitterspacetimedeSitterspacetimet=0time

FIG. 2: Mapping between the de Sitter universe and complex networks. Panel (a) shows the 1 + 1-dimensional de Sitterspacetime represented by the upper half of the outer one-sheeted hyperboloid in the 3-dimensional Minkowski space XY Z.The spacetime coordinates (, t), shown by the red arrows, cover the whole de Sitter space. The spatial coordinate of anyspacetime event, e.g. point P, is its polar angle, while its time t is the length of the arc connecting the point to the XY planewhere t = 0. At any time t, the spatial slice of the spacetime is a circle. This 1-dimensional space expands exponentiallywith time. Dual to the outer hyperboloid is the inner hyperboloidthe hyperbolic 2-dimensional space, i.e. the hyperbolicplane, represented by the upper sheet of a two-sheeted hyperboloid. The mapping between the two hyperboloids is shown bythe blue arrows. The green shapes show the past light cone of point P in the Sitter space, and the projection of this lightcone onto the hyperbolic plane under the mapping. Point P is causally connected to all the spacetime quanta lying within itsgreen shape. Panel (b) depicts the cut of panel (a) by the Y Z plane to further illustrate the mapping, shown also by the bluearrows. The mapping is just the reection between the two hyperboloids with respect to the cone shown by the dashed lines.Panel (c) projects the inner hyperboloid (the hyperbolic plane) with the Ps past light cone (the green shape) onto the XYplane. The red shape is the left half of the hyperbolic disc centered at P and having the radius equal to Ps time t, whichin this representation is Ps radial coordinate, i.e. the distance between P and the origin of the XY plane. In the hyperbolicmodel of complex networks [18], a new node P connects to all the existing nodes lying within this red shape. The green andred shapes become indistinguishable at large times t, establishing the equivalence between the causal structure of the de Sitteruniverse, and the large-scale structure of complex networks.4100101102103104108106104102100node degree kdegree

distribution

P(k)

BrainInternetde SitterP(k) ~ k2FIG. 3: Degree distributions in the brain, Internet, and de Sitter spacetime. The degree distribution P(k) is the number ofnodes N(k) of degree k divided by the total number of nodes N in the networks, P(k) = N(k)/N. The brain is a functionalnetwork of the human brain obtained from the fMRI measurements in [21]. The Internet is the network representing economicrelations between autonomous systems, extracted from CAIDAs Internet topology measurements [22]. De Sitter is a causal setin the 1 + 1-dimensional de Sitter spacetime. The sizes of the three networks are, respectively, 23713, 23752, and 23739, andtheir average node degrees are 6.14, 4.92, and 5.65. All further details are in Section I.10-510-410-310-210-1100101rescaled degree 10-610-410-2100102104degree

3/. As shown in Section III, the rescaled degree distribution does not depend on either or a, so we set them to = 104and a = 1 for convenience. The size N of simulated causets can be also set to any value without aecting the degreedistribution, and this value is N = 106nodes in the gure. The degree distribution in this simulated causet is juxtaposedagainst the numeric evaluation of the analytical solution for Q(, 0) shown by the blue dashed line. The inset shows thisanalytic solution for the whole range of node degrees k [1, 10244] in the universe, where 10173and a 5 1017. Panel (b)shows the same solution for dierent values of rescaled time , tracing the evolution of the degree distribution in the universe.All further details are in Section III.energy-dominated era. The part of the distribution with exponent 3/4 freezes, while the soft cut-o transforms intoa crossover to another power law with exponent 2, whose cut-o grows exponentially with time. The crossover pointis located at kcr a4, where 1/(l3PtP) is the density of spacetime quanta. Nodes of small degrees k < kcr obeythe = 3/4 part of the distribution, while high-degree nodes, k > kcr, lie in its = 2 regime. At the future innity , the distribution becomes a perfect double power law with exponents 3/4 and 2. The detailed analysis ofthese statements and their proofs can be found in Section III.5In summary, the asymptotic causal structure of the de Sitter universe is unexpectedly similar to the large-scalestructure of many complex networks. As the universe expands into the future, its causal set grows and the dynamicsof this growth is asymptotically the same as the dynamics governing the large-scale evolution of the Internet andother networks. Geometrically, this equivalence is due to a simple duality between the two hyperboloids in Fig. 2.The inner hyperboloid represents the geometry of complex networks; the outer hyperboloid is the de Sitter spacetime,which is the solution to Einsteins equations for a universe with positive vacuum energy. In that sense, the Einsteinequations provide an adequate description for the evolution of the Internet and other complex networks. Collectivelythese ndings suggest that unexpectedly similar mechanisms whose precise nature and common origin remain to beidentied, may shape the large-scale structure and dynamics of complex systems as dierent as the brain, the Internet,and the universe.6I. DATA AND SIMULATIONSIn this section we describe the data and methods used in Figs. 3&4. We note that in all the three considerednetworks, links represent soft relational data instead of hard-wired network diagrams: causal/correlation relations inthe brain, economic/business relations in the Internet, and causal relations in causal sets.A. Brain dataThe brain functional network in Fig. 3 represents causal/correlation relations between small areas in the humanbrain. This network is extracted from the fMRI measurements in [21]. In those experiments, the whole brains ofdierent human subjects are split into 36 64 64 = 147456 adjacent areas called voxels, each voxel of volume3 3.475 3.475 mm3. The subjects are then asked to perform dierent tasks, during which the magnetic resonanceactivity V (x, t) is recorded at each voxel x at time t. Time t is discrete: 400 recordings are made with the interval of2.5 s. Given this data, and denoting by the time average, the correlation coecient r(x, x

) for each pair of voxels

is then computed:r(x, x

) = V (x, t)V (x

, t) V (x, t)V (x

, t)_[V (x, t)2 V (x, t)2] [V (x

, t)2 V (x

, t)2]. (1)To form a functional network out of this correlation data, two voxels x and x

are considered causally connected if the

) > rc. If rc is too small or too large, thenthe resulting network is fully connected or fully disconnected. There exists however a unique percolation transitionvalue of rc corresponding to the onset of the giant component in the network. To nd this value, we compute the sizes|S1|rc and |S2|rc of the largest and second largest components S1 and S2 in the network for dierent values of rc. Thevalue of threshold rc corresponding to the largest values of ||S1|/rc|rc and |S2|rc is then its percolation transitionvalue. With threshold rc set to this value, the network has a power-law degree distribution with exponent = 2and exponential cutos, and this result is stable across dierent human subjects and dierent types of activity thatthey perform during measurements, see [21, 27] and http://www.caida.org/publications/papers/2012/network_cosmology/supplemental/. In Fig. 3, a specic dataset is usedSet14, see the URLwhere the subject is at rest.The threshold value is rc = 0.7.B. Internet dataThe Internet topology in Fig. 3 represent economic/business relations between autonomous systems or ASs. The ASis an organization or an individual owning a part of the Internet infrastructure. The network is extracted from the datacollected by CAIDAs Archipelago Measurement Infrastructure (Ark) [22], http://www.caida.org/projects/ark/.The Ark infrastructure consists of a set of monitors continuously tracing IP-level paths to random destinationsin the Internet. The union of the paths collected by all the monitors is then aggregated over a certain periodof time, and each IP address in the collection is mapped to an AS owning this address, using the RouteViewsBGP tables http://www.routeviews.org/. The resulting AS network has a power-law degree distribution withexponent = 2.1, and this result is stable over time [28, 29] and across dierent measurement methodologies [30, 31],http://www.caida.org/publications/papers/2012/topocompare-tr/. The data and its further description areavailable at http://www.caida.org/data/active/ipv4_routed_topology_aslinks_dataset.xml. In Fig. 3, thedata for June 2009 is used. The aggregation period is one month, and the number of monitors is 36.C. De Sitter causetThe causet in Fig. 3 is generated by sprinkling a number of points over a patch in the 1 + 1-dimensional de Sitterspacetime, and connecting each pair of points if they lie within the others light cones. The point density is uniformin the de Sitter metric, and the size of the patch is such that the size of the generated causet and its average degreeare close to those of the brain and the Internet.In conformal time coordinates, see Section II A below, each spacetime point has two coordinates, spatial [0, 2]and temporal (/2, /2), with = /2 and = /2 corresponding to the past and future innities respectively.The spacetime patch that we consider is between = 0 and = 0 > 0, where 0 is determined below. This patchis illustrated in Fig. 2(a). To sample N points from this patch with uniform density, we sample N pairs of random70 5 10 1500.050.10.150.20.250.30.35node outdegree kout

degree

distribution

Po(k)

Poissoniandata(a)100101102103104108106104102100node indegree kin

degree

distribution

Pi(k)

d = 1d = 3Pi(k) ~ k2(b)FIG. 5: (a) The out-degree distribution of the causet approximated by a patch of 1+1 dimensional de Sitter spacetime. Thesolid line shows the Poissonian distribution with the mean =

ko =

k/2 = 2.83, where

ko is the average out-degree in thecauset. (b) The in-degree distribution of causets approximated by patches of 1+1 and 3+1 dimensional de Sitter spacetime.The causet sizes are N = 23739 and N = 23732, and their average degrees are

) are then connected in the causet if < , where

= | |

|| and = |

| are the spatial and temporal distances between the points in this coordinatesystem.To determine 0 we rst note that since the point density is uniform, the number of points N is proportional to thepatch volume, and the proportionality coecient is a constant point density . The volume of the patch is easy tocalculate, see Section II A, where we can also calculate the average degree

k in the resulting causets, so that we have:N = 2a2tan 0, (3)

k = 4a2_

0tan 0+ ln sec 01_, (4)

kN = 2

0/ tan 0 + ln sec 01tan 0, (5)where a is the spacetimes pseudoradius determining its curvature. Given a target average degree

k and number ofnodes N in a causet, their ratio determines 0 via the last equation. Sampling N points from this patch will thenyield causets with expected average degree

k. The average degree in generated causets will not be exactly equal to

k since there will be nodes of degree 0 and Poissonian uctuations of the numbers of nodes lying within light conesaround their expected values. In Fig. 3, the exact number of sampled points is N = 24586 and target value of theaverage degree is

k = 5.53, so that the above equations yield 0 = /2 3.86 105and a2= 0.151. The resultingnumber of nodes excluding nodes of degree 0 and their average degree in the generated causet are reported in thecaption of Fig. 3.Figure 3 shows the in-degree distribution in the causet, with links oriented from the future to the past, i.e. fromnodes with larger to nodes with smaller . The out-degree distribution is not particularly interesting, close toPoissonian, see Fig. 5(a).In higher dimensions, pre-asymptotic eects become more prominent in causets of similar size and average degree.In particular, the exponent of the degree distribution is slightly below 2, see Fig. 5(b) comparing the causets in thed = 1 and d = 3 cases. The former causet is the same as in Fig. 3, while the latter is obtained by a procedure similar8to the one described above, except that instead of Eqs. (2-5), we have(|0) = 3 sec4

(2 + sec2

0) tan 0, (6)N = 23

2a4_2 + sec2

0_tan 0, (7)

k = 49a412 (0/ tan 0 + ln sec 0) + (6 ln sec 05) sec2

072 + sec2

0, (8)

kN = 23

12 (0/ tan 0 + ln sec 0) + (6 ln sec 05) sec2

07(2 + sec2

0)2tan 0, (9)and each point has two additional angular coordinates, 1 and 2, which are random variables between 0 and drawnfrom distributions (2/) sin2

1 and (1/2) sin 2, see Section II A. The spatial distance between pairs of pointsis then computed using the spherical law of cosines, and two points are causally linked if < as before. InFig. 5(b), the d = 3 causet has target N = 25441 and

k = 5.29, yielding 0 = /2 4.11 102and a4= 0.267.The maximum-likelihood tting of the degree sequence in the d = 3 and d = 1 causets yields = 1.65 and = 1.90,while the least-square tting of the complementary cumulative distribution function for node degrees yields = 1.77and = 1.98.D. Simulating the universeThe results in Section III below allow us to simulate the universe causet similarly to the de Sitter simulationsdescribed in the previous section. The main idea behind the universe simulations is that we can use the exact resultsfor a at universe, but apply them to a closed universe with an arbitrary nite number of nodes in its causet, sincethe real universe is almost at, and since the degree distribution in both cases is the same.Specically, the causet in Fig. 4 is generated as follows. Unlike the previous section where only conformal time isused, here it is more convenient to begin with the rescaled time = t/a. The current measurements of the universeyield, see Section III C, 0 = t0/a = (2/3) arcsinh_

/M = 0.8458 as the best estimate for this rescaled time inthe universe today. According to Eqs. (85,86), the scale factor isR() = sinh2332, (10)where is a free parameter that we can set to whatever value we wish, since the degree distribution does not dependon it, see Section III E. We wish to set to the value such that the generated causet would have a desired number ofnodesN = 13

3(sinh 3030) , (11)where , the node density, is yet another free parameter that does not aect the degree distribution according toSection III E. The scale factor in Eq. (10) means that the temporal coordinate that we assign to each of these N nodesis a random number [0, 0] drawn from distribution(|0) =6 sinh2 32

sinh 3030. (12)Having times assigned, we then map them, for each node, to conformal times via = 23_ 32

0dxsinh23x. (13)The spatial coordinates , 1, and 2 are then assigned exactly as in the previous section, and the causet networkis also formed exactly the same way, i.e. by futurepast linking all node pairs whose temporal distance exceedstheir spatial distance .Figure 4 shows the in-degree distribution for a causet generated with = 2.01 and = 104. The resulting numberof nodes in the causet is N = 106. The analytic solution curves are obtained using the approximations for the in-degree distribution derived in Section III. The key equations are Eq. (70), yielding the Laplace approximation for the90 200 400 600 800node out-degree ko0123out-degree

distribution

P(ko

,t0)SimulationsAnalytic solutionx10-3FIG. 6: Out-degree distribution in the simulated universe.in-degree distribution, and Eq. (91) expressing conformal time as a function of the current value of the scale factor.The precise steps to numerically compute the analytic solution for the in-degree distribution are listed in Section III G.The perfect match between the simulations and analytic solution in Fig. 4 conrms that the approximations used inSection III to derive the analytic solution yield very accurate results.The out-degree distribution in the same simulated causet is shown in Fig. 6. The analytic solution is obtained bynumeric evaluations of Eqs.(62-66). The distribution appears to be uniform over a wide range of degree values.10II. ASYMPTOTIC EQUIVALENCE BETWEEN CAUSAL SETS IN DE SITTER SPACETIME ANDCOMPLEX NETWORKS IN HYPERBOLIC SPACEA. De Sitter spacetimeThe d + 1-dimensional de Sitter spacetime [16] is the exact solution of the Einstein equationsG

d1d2d (23)is the metric on Sd. That is, at each time t, the universe is a sphere of exponentially growing radius a cosh(t/a) andvolumev = d_a cosh ta_d, where d = 2d+12

_d+12_ (24)11

= =0 AB=/2=0 t=0t= '

FIG. 7: Causal structure of the d + 1-dimensional de Sitter spacetime. d 1 dimensions are suppressed, so that each pointrepresents a d 1-sphere. is the distance between A and B on the d-dimensional unit sphere Sd.is the volume of Sd. Figure 2 visualizes this foliation for d = 1, in which case these time-slice spheres are circles, andtheir volume is the circles circumference.To study the causal structure of de Sitter spacetime, it is convenient to introduce conformal time (

with the (, ) axes, see Fig. 7. Therefore,

) lies in the past light cone of point B at time

(0, /2) if the angular

distance between A and B on Sdis less than the conformal time dierence

between them: <

= arccos 1cosh t

aarccos 1cosh ta, or approximately (27) < 2_e

tae

a_ 2e

ta, (28)where the last approximation holds for t

t 1, and where we have used the approximation =

arccos[1/ cosh(t/a)] /2 2et/a, which is valid for large t.The volume form on de Sitter spacetime in the conformal and cosmological time coordinates is given bydV =_ acos _d+1d dd =_a cosh ta_ddt dd

_a2_dedatdt dd, where (29)dd = sind1

1 sind2

2 . . . sin d1d1 d2 . . . dd (30)is the volume form on Sd, i.e._ dd = d.B. Hyperbolic spaceThe hyperboloid model of the d+1-dimensional hyperbolic space [33] is represented by one sheet of the two-sheetedd + 1-dimensional hyperboloid of constant negative scalar R and Gaussian K curvaturesz20 + z21 + . . . + z2d+1 = b2= d(d + 1)R = 1K (31)borrowing its metric from the d +2-dimensional ambient Minkowski space with metric (16). This hyperboloid in thed = 1 case is visualized as the inner hyperboloid in Fig. 2. As a side note, the curvature of the same hyperboloid in12the Euclidean metric is everywhere positive but not constant. The standard coordinate system (r, 1, . . . , d) on thishyperboloid is given byz0 = b cosh rb, (32)z1 = b sinh rb cos 1, (33)...zd = b sinh rb sin 1 . . . sin d1 cos d, (34)zd+1 = b sinh rb sin 1 . . . sin d1 sin d, (35)where r R+ is the radial coordinate, and 1, . . . , d1 [0, ] and d [0, 2] are the standard angular coordinateson the unit d-dimensional sphere Sd. The radial coordinate r of point P on the hyperboloid is the Minkowski lengthof the arc connecting P to the hyperboloid vertex, which is the bottom of the inner hyperboloid in Fig. 2. As aside note, the Euclidean length rE of the same arc is given by the incomplete elliptic integral of the second kindrE = iE(ir, 2).C. Hyperbolic model of complex networksIn the hyperbolic model of complex networks [18], networks grow over the d + 1 dimensional hyperbolic space ofGaussian curvature K = 1/b2according to the following rule in the simplest case. New nodes n are born one at atime, n = 1, 2, 3, . . ., so that n can be called a network time. Each new node is located at a random position on Sd.That is, the angular coordinates (1, . . . , d) for new nodes are drawn from the uniform distribution on Sd. The radialcoordinate of the new node isr = 2bd ln n

, (36)where is a parameter controlling the average degree in the network. Upon its birth, each new node connects to allthe nodes lying within hyperbolic distance r from itself. In other words, the connectivity perimeter of new node nat time n is the hyperbolic ball of radius r centered at node n. The hyperbolic distance x between two points withradial coordinates r and r

located at angular distance is given by the hyperbolic law of cosines [33]:

x = b arccosh_cosh rb cosh r

b sinh rb sinh r

b cos _ r + r

+ 2b ln

2 . (37)Therefore new node n

connects to existing nodes n < n

whose coordinates satisfy

,with = 2. Indeed, according to Eq. (36), the radial density of nodes at any given time scales with r as (r) er,where = d/(2b). One can also calculate, see [34], the average degree of nodes at radial coordinate r, which is

k(r) er, where = . The probability that a node at r has degree k is given by the Poisson distribution withthe mean equal to

k(r). Taken altogether, these observations prove that = / +1 = 2. The networks in the modelalso have strongest possible clustering, i.e. the largest possible number of triangular subgraphs, for graphs with thisdegree distribution, and their average degree is

k 2d+1

d ln n, where d =

d1d (40)is the volume of the unit d-dimensional ball. The model and its extensions describe the large-scale structure and growthdynamics of dierent real networks, e.g. the Internet, metabolic networks, and social networks, with a remarkableaccuracy [18].13r = 5 r = 10 r = 15FIG. 8: The hyperbolic disc (red) given by Eq. (38) of time radius r

= t

centered at the black point representing a new node

in Section II C vs. its past de Sitter light cone (green) given by Eq. (27) for dierent values of r

in the d = 1, b = 1 case.Assuming the average degree of

k = 10, the shown time radii correspond to network sizes of approximately 40, 200, and 2000nodes.D. Duality between de Sitter causal sets and complex networksTo demonstrate the asymptotic equivalence between growing networks from the previous section and causal setsgrowing de Sitter spacetime, we:1. nd a mapping of points in the hyperbolic space Hd+1to de Sitter spacetime dSd+1such that:2. the hyperbolic ball of new node n Hd+1is asymptotically identical to its past light cone in dSd+1upon themapping, and3. the distribution of nodes after mapping is uniform in dSd+1.1. MappingA mapping that, as we prove below, satises the properties above is remarkably simple:t = r, (41)a = 2b. (42)That is, we identify radial coordinate r in Hd+1with time t in dSd+1, keeping all the angular coordinates the sameseeFig. 2 for illustration.2. Hyperbolic balls versus past light conesThe proof that the hyperbolic balls of new node connections map asymptotically to past light cones is trivial:inequalities (39) and (28) are identical with the mapping above. See Fig. 8 visualizing the approximation accuracy atdierent times.3. Uniform node densityThe proof that the node density after mapping is uniform in dSd+1is trivial as well. Indeed, according to Eq. (36)with 2b = a, network time n is related to cosmological time t vian = edat, so that (43)dn = daedatdt. (44)14Using the last equation, we rewrite the de Sitter volume element in Eq. (29) asdV = 2bd+1d dndd. (45)By construction in Section II C, the node density on Sdis uniform and equal to 1. Therefore the number of nodes dNin element dndd isdN = 1

ddndd. (46)Combining the last two equations, we obtaindN = dV, where (47) = d2bd+1

d, (48)meaning that nodes are distributed uniformly in dSd+1with constant density , thus completing the proof.A causal set growing in de Sitter spacetime with node density in Eq. (48) is thus asymptotically equivalent to agrowing complex network in Section II C with average degree

k in Eq. (40). Combining these two equations, we canrelate and

k to each other:

k 2dadt. (49)An important consequence of this asymptotic equivalence is that the degree distribution in both cases is the samepower law with exponent = 2.15III. THE UNIVERSE AS A CAUSAL SETDe Sitter spacetime is the spacetime of a universe with positive vacuum (dark) energy density and no matter orradiation. Since the real universe does contain matter, its spacetime deviates from the pure de Sitter spacetime. Atearly times, matter dominates, leading to the Big Bang singularity at t = 0 that de Sitter spacetime lacks. At latertimes, the matter density decreases, while the dark energy density stays constant, so that it starts dominating, and theuniverse becomes asymptotically de Sitter. The universe today is at the crossover between the matter-dominated anddark-energy-dominated eras, since the matter and dark energy densities M and

are of the same order of magnitude,

leading to rescaled cosmological time = t/a 1the so-called why now? puzzle in cosmology [2426].To quantify these deviations from pure de Sitter spacetime, and their eect on the structure of the causal set of thereal universe, we calculate its degree distribution in this section. This task is quite challenging, and in what followswe rst provide the exact analytic expression for the degree distribution, and then derive its approximations based onthe measured properties of the universe. These approximations turn out to be remarkably accurate because accordingto the current measurements, the universe is almost at.We emphasize that the approximations in this section are based on the exact solution of the Einstein equations fora at universe containing only matter and constant positive vacuum energy, i.e. constant cosmological constant ,and that we assume that the universe is governed by this solution at all times. This assumption is a simplicationof reality for a number of reasons. For example, there are a plenty of cosmological scenarios, such as cosmic bubblecollisions [35], in which the ultimate fate of the universe deviates from the asymptotic solution for the universetoday. There are also a variety of models with non-constant , yet the standard CDM model with constant isa base line describing accurately many observed properties of the real universe [36], partly justifying our simplifyingassumption. We also note that by relying on the exact solution for a universe containing only matter and , weeectively neglect the earliest stages of universe evolution such as the radiation-dominated era or inationary epoch.There is no consensus on how exactly the universe evolved at those earliest times, but since the radiation-dominatedera ended soon after the Big Bang [37], these details unlikely have a profound eect on the universe causets structureat much later times.A. Exact expression for the degree distributionThe metric in a homogeneous and isotropic universe takes the FLRW form:ds2= dt2+ R2(t)_d2+ 1K sin2_

K__sin2 d2+ d2__, where (50)1K sin2

K =_

_sin2 if K = 1 (closed universe with positive spatial curvature),

2if K = 0 (at universe with zero spatial curvature),sinh2 if K = 1 (open universe with negative spatial curvature).(51)In the above expression, coordinates [0, 2] and [0, ] are the standard angular coordinates on S2, while [0, ] if K = 1, or [0, ] if K = 0, 1, is the radial coordinate in a spherical, at, or hyperbolic space. Finally,t [0, ] is the cosmological time, and R(t) is the scale factor, nding an appropriate approximation to which inthe real universe is an important part of our approximations in subsequent sections. In this and the next sections,however, all the expressions are valid for any scale factor.As with pure de Sitter, it is convenient to introduce conformal time , related to cosmological time t via =_ t0dt

K_sin d dd d. (54)As with pure de Sitter, we orient edges in the universes causet from future to the past. Therefore if is the currentconformal time, then the average in- and out-degrees

ki,o(

|) of nodes born at conformal time

< are simply

16proportional to the volumes of their future and past light cones Vf,p(

|):

ki(

|) = Vf(

|), (55)

ko(

|) = Vp(

|), (56)where the coecient of proportionality is the Planck-scale node density in the spacetime, which we take to be theinverse of the Planck 4-volume: = 1t4P= 1.184 10173s4, where (57)tP = 5.391 1044s (58)is the Planck time.Thanks to conformal time coordinates, the expressions for volumes Vf,p(

|) are easy to write down:

Vf(

|) = 1K_

R4(

)_

0d sin2_

K__

0d sin _ 20d, (59)Vp(

|) = 1K_

0d

R4(

)_

0d sin2_

K__

0d sin _ 20d. (60)Computing the three inner integrals, we obtain

ki(

|) =

K_

_2(

) 1

Ksin_2

K(

)__R4(

) d

, (61)

ko(

|) =

K_

0_2(

) 1

Ksin_2

K(

)__R4(

) d

. (62)As shown in [11], Lorentz invariance implies that nodes/events of the causet are distributed in spacetime according toa Poisson point process. Therefore, as explained in [38], to nd the in- or out-degree distributions P(k, ) at time ,we have to average the Poisson distributionp(k|

, ) = 1k!_

k(

|)

ke

k(

|), (63)which is the probability that a node born at time

has degree k, with the density of nodes born at time

|) = R4(

)N() , where (64)N() =_

0R4(

)d

(65)is the time-dependent normalization factor. The result isP(k, ) =_

0p(k|

, )(

|) d

= 1N()1k!_

0_

k(

|)

ke

k(

|)R4(

) d

. (66)The above expressions are valid for both in-degree (set k ki and

k(

|)

ki(

|)) and out-degree (k k

o,

k(

|)

ko(

|)) distributions. In what follows we focus on the in-degree distribution.

B. First approximation using the Laplace methodEquations (66,61) give the exact solution for the in-degree distribution with an arbitrary scale factor R(), but itis dicult to extract any useful information from these expressions, even if we know the exact form of R(). The rststep to get a better insight into the causet properties is to rewrite

ki(

|) as

ki(

|) =

ki(0|)F(

|), where F(

|) =

ki(

|)

ki(0|) . (67)17The in-degree of the oldest node

ki(0|) is an astronomically large number, both because the node is old, so thatits future light cone comprises a macroscopic portion of the 4-volume of the whole universe, and because the degreeis proportional to huge . However, function F(

|) is a monotonically decreasing function whose values lie in the

interval [0, 1]. We can thus use the Laplace method to approximate the integral in Eq. (66) in the limit 1.Introducing

(ki), which is the solution of the transcendent equationki =

ki(

|), (68)and function(ki, ) =

ki(

|)

(ki), (69)the result of this Laplace approximation reads:P(ki, ) =_

__ 3

_14

_54_ R3()N() if ki = 0,1N()

2kikkii ekiki!R4[

(ki)](ki, )

1N()R4[

(ki)](ki, ) if 1 ki

ki(0|),(70)where we have also used Stirlings approximation k!

2k(k/e)k. We see that the shape of the degree distributionis almost fully determined by

(ki), and that the distribution is a fast decaying function for degrees above

ki(0|).The expression for the in-degree distribution in Eq. (70) is now more tractable and gets ready to accept the scalefactor R() of the universe. Unfortunately, the exact expressions for the scale factor of a closed or open universewith matter and dark energy, although known [39], resist analytic treatment, and so does the integral for the averagedegree

ki(

|) in Eq. (61) that we are to use in Eq. (70). Therefore we next develop a series of approximations tothe scale factor and average degree, based on the measured properties of the universe.C. Measured properties of the universeThe current measurements of the universe [36] that are relevant to us here include:

H0 [5.00, 5.32] 1017s, is the de Sitter pseudoradius. (78)The range of values of

, M, H0, and t0 are given by the 95% condence bounds in the universe measurements takenfrom the last column of Table 1 in [36], while the values of K come from Table 2 there: third row, last column. Thematter density M is the sum of two contributions: the observable (baryon) matter density (b [0.0442, 0.0474]),and dark matter density (c [0.214, 0.244]). The radiation density is negligible. The universe thus consists ofdark energy ( 73%), dark matter ( 23%), and observable matter ( 4%). As a side note, the sum of all s

[0.95, 1.04] as expected, since

= 1 is the Einstein/Friedmann equation for a FLRW universe, which we

recall in the next section. Much more important for us here is that the universe is almost at, K 0.18D. Approximations to the scale factor and average degreeIn this section we utilize the approximate atness of the universe to derive and quantify approximations to the scalefactor of the universe and average degree in Eq. (61). The main idea is that since K is small, we can use the exactsolution for the scale factor in a at universe (K = 0) with matter and dark energy (positive cosmological constant),which is actually quite simple [16].We rst recall that the 00-component of the Einstein equations in the FLRW metric is the rst Friedmann equation:

R2+ KR2 = 83G_

+_R0R_3

M_. (79)This equation can be rewritten [40] in the following form:H0t =_ R/R00dxx

1 + x2+ x3. (84)Solving it for r r(, ), we conclude that the solution for the scale factor becomesR(t) = r (; ) . (85)This is denitely not the only way to write down the scale factor in terms of the parameters of the universe. Yetwritten in this form, the rescaled scale factor r(; ) is a dimensionless function of its dimensionless arguments, andparameter is close to zero, so that r(; ) r(; 0), where r(, 0), i.e. the rescaled scale factor for a at universewith positive matter and dark energy densities, is quite simple:r(; 0) = sinh2332. (86)Figure 9(left) shows a good agreement between the numerical solution for r(; ) and the analytical expression forr(; 0) in Eq. (86) for the range of the values of allowed by measurements in Eq (83). Therefore in what follows wecan use Eq. (86), even if the universe is slightly closed or slightly open.To approximate the average degree

ki(

|) in Eq. (61), we rst compute, using the scaling Eq. (85), the conformaltime =_ t0dt

= 0.5403 for the closed universe with = 0.0368 and

= 0.4260 for the open universe with = 0.0232. If

0, yet if is exactly zero, then

is an undened constant, since in the at case, R

0is a free parameter that can be set to an arbitrary value without aecting anything. If the universe is not exactly at,190 1 2 3 4 5

00.511.5relative

error

(%)=-0.0368=0.02320 0.1 0.2 0.3 0.4 0.5 0.6x01234567relative

error

(%)K=1K=-1FIG. 9: Left: Relative error between functions r(; ) and r(; 0), |r(; ) r(; 0)|/r(; ), for the two extremes of the 95%condence interval of parameter in Eq. (83), as a function of rescaled time . In the worst case, the relative error is around 1%.Right: Relative error of approximation in Eq. (88) for x [0, ].then the above values of

allowed by measurements are well below /2, so that we can safely replace expression_2x sin(2

Kx)/

K_/K in Eq. (61) by its Taylor expansion around zero,1K_2x 1

Ksin(2

Kx)_

43x3. (88)Indeed the maximum possible relative error between the left and right hand sides in the last equation at x =

isaround 6%, see Fig. 9(right), whereas such error is zero for an exactly at universe with = K = 0, since the left andright hand sides are equal in this case. Therefore the approximation to the average degree in Eq. (61)

ki(

|) = 43

)3R4(

) d

(89)is exact for at universes, and almost exact for slightly closed or open universes with within the range of Eq. (83).E. Scaling relationsTo proceed to our nal approximations to the degree distribution in the next section, we derive some scaling relationsthat tremendously simplify the calculations, provide important insights into the properties of the degree distribution,and suggest methods to simulate the causal set of the universe.We begin with the scaling relation for conformal time . Let us introduce the rescaled conformal time

a =

_||, (90)and let notations y = f1,2,...(x) mean y is a function of x. Given the denition of conformal time in Eq. (87), thisrescaled conformal time is a function of rescaled cosmological time : = f1() =_ r(;)0dxx2

1 + x2+ x3

02_r(; 0) 2F1_16, 12, 76, r3(; 0)_, (91)where r(; 0) is given by Eq. (86), and 2F1 is the hypergeometric function. This scaling means thatR() = f2(), (92)which in turn implies that the average in-degree of nodes born at time

ki(

|) = qf3 (

, ) = qf4 (

, ) , where (93)q a4, (94)20while the average in- or out-degree in the causet

kiq , (97)and the rescaled in-degree distribution Q(i, ) is some function of rescaled degree i and time . This result isimportant for several reasons:1. It denes a characteristic degree q = a4and characteristic time a such that the degree distribution dependsonly on the rescaled dimensionless variables i = ki/q and = t/a. Therefore we are free to set a = 1 and = 1, and the remaining task is to nd an explicit form of the rescaled distribution Q(i, ) in Eq. (96).2. Even though the scale factor in Eq. (85) does depend on , the in-degree distribution P(ki, t) does not dependon . This is important from the practical point of view, because we can carry out all the calculations setting = 1 as well.3. Having set = a = = 1, the in-degree distribution explicitly does not depend on any physical parameters ofthe universe. The causets in at or open universes are graphs with obviously innite numbers of nodes from thevery beginning of the universe, while the causets of closed universes are always nite, and the number of nodesin them depend on time and on all the three parameters , a, and . Yet the in-degree distributions in all thesecausets at a given time are all the same, and in particular, do not depend on the number of nodes at all. Thissomewhat unexpected result deserves further explanation. We note that parameter = R03_

M/

dependson the physical properties of the universe. In particular, if the universe is closed, it denes, via R0 in Eq. (73),the radius of the three-sphere, i.e. of the spatial part of the FLRW metric. Since the degree distribution doesnot depend on , we can freely take the limit corresponding to a at space, without aecting the degreedistribution. In other words, we are free to choose a closed universe with K = +1 in Eq. (50), and this choicecan be considered as a degree-distribution-preserving compactication of an innite at or hyperbolic spacewith an innite number of nodes in it, into a compact spherical space with a nite number of nodes.4. The previous point informs us how to simulate the universe causet on a computer. We cannot simulate innitecausets for at or open universes, but since the degree distribution is the same in closed universes, we are freeto simulate the latter. One can check that in a closed universe, the number of nodes in a causet grows withtime asN(t) = a3f7 () . (98)We can set a = 1, and since the in-degree distribution does not depend on the number of nodes N, then forany given time t = a = and for any given value of , we can generate graphs with any desired size Nby choosing the value of accordingly. The degree distribution obtained from this simulation is then to bere-scaled according to Eq. (96) to obtain the rescaled distribution Q(i, ). Keeping xed t = and N, we canalso explore dierent regions of Q(i, ) by changing the value of .We have to emphasize that the points above, and the scaling relations in this section, are valid for non-at universesonly when

is well below /2, so that the approximation in Eq. (88) is valid for all x [0,

]. When this condition

does not hold, e.g. when is large and the universe is far from being at, then all these scaling relations breakdown. In particular, we cannot claim that in that case the in-degree distribution would still be the same. The latestmeasurements of the universe parameters indicate that the scaling laws derived in this section do apply to the realuniverse, but even if this were not the case, these laws would still be valid for small values of conformal time.F. Final approximations to the degree distribution in the matter- and dark-energy-dominated erasWe now have all the material needed to derive the nal approximations to the degree distribution. According toSection III D the exact solution for the scale factor in a at universe is a good approximation for the scale factor in21the real universe within the measurement-allowed range of parameter values, so that we will use this approximationhere, i.e. we set = 0. Using the scaling results from Section III E, we also set a = = = 1, which is equivalentto working with rescaled cosmological time , rescaled conformal time , rescaled scale factor r() = sinh2/3(3/2),and rescaled in-degree i. Since everything is rescaled in this section, we omit word rescaled in front of the variablenames.We consider two eras of the universe evolution. The rst era characterizes the causal set of the universe at earlytimes, when matter dominates. The second era deals with the aged universe at large times, when dark energydominates. In the limit of innite time (future innity), we derive the exact solution for the degree distribution in aat universe.1. Matter-dominated eraIn this era with 1, the scale factor and conformal time can be approximated byr() =_32

_23(99) = 2_32

_13, so that (100)r() =_

2_2. (101)Using these expressions in the rescaled version of Eq. (89), we obtain the average in-degree at time of nodes bornat time

:

i(

|) =

768

12_ 112

311

+ 310_

_2

19_

_3+ 11980_

_12_. (102)The maximum average in-degree, i.e. the average in-degree of the oldest nodes, is

i(0|) =

9216

12= 94. (103)We note that if we reinsert constants a and , and cosmological time t using Eq. (93), the average in-degree of theoldest nodes becomes

ki(0|t) = 9_ ttP_4, (104)where tP is the Planck time, see Eq. (57). As expected, this in-degree does not depend on a and, consequently, onthe cosmological constant .For degrees well below the maximum degree, i 94, the Taylor expansion of Eq. (102) around

= yields

i(

|)

3072

12_

1_4. (105)Using this expression in the rescaled version of Eq. (70), we obtainQ(i, ) = 34_3

_14

3/4i

if i 94, (106)and, after reinserting all the physical constants,P(ki, t) = 34_3

_14tPt k3/4i if 1 ki 9_ ttP_4, (107)with a soft cut-o at 9 (t/tP)4. We thus observe that the degree distribution does not depend on the cosmologicalconstant , and that it is a power law P(ki, ) k

i with exponent = 3/4.222. Dark-energy-dominated eraWe now analyze the future fate of the universe at 1, which is slightly more intricate. To begin, we derivea couple of expressions that we use in simulations and that are valid for any . We rst recall that, according toEq. (91), conformal time is related to the current value of the scale factor r via(r) = 2

r 2F1_16, 12, 76, r3_, (108)which is a monotonously increasing function of r. Therefore we can use scale factor value r as a measure of timeinstead of . Using this observation in Eq. (89), we write the average in-degree at time r of nodes born at time r

|r) = i.Assuming now that time is large, we see from Eq. (109) that the maximum average in-degree scales at r 1 as

i(0|r) = 43_ r0

3(x) x2dx

1 + x3

43

3

_ r0x2dx = 49 (

r)3, where (111)

= () =_

0dxx2

1 + x3= 2

_13_

_76_ (112)is the conformal time at the future innity. Since r grows exponentially with time in this era, so does the averagein-degree of the oldest nodes according to Eq. (111). In the long time limit, and for degrees well below the maximumaverage degree, i

i(0|r), we have r > r

1. Keeping the rst two terms in the Taylor expansion of Eq. (108)for r 1, we approximate conformal time by (r)

1/r. Inserting this approximation into Eqs. (109,110), and

neglecting x3there, we obtain the implicit expression for the in-degree distribution at the future innity,Q(i, ) = 941x(i)3[x(i) 1]3, (113)where x(i) is the solution of equation

i = 29_(x 1)(2x27x + 11) 6 ln x

, (114)in the region x 1. The last two equations give a nearly exact solution for the asymptotic in-degree distribution ina at universe, because all the approximations that we have made so far become exact in the t, r limit. Theonly approximation that is not rigorously exact is the one due to the Laplace method in Section III B, yet given theastronomical value of node density in Eq. (57) used in this approximation, it can be also considered exact. We alsonote that the distribution is properly normalized since_

1 Q(x, ) (i)

x dx = 1.Finally, we nd approximations to this exact solution for small and large degrees. If i 1, then the solution ofEq. (114) scales as x(i) = 1 +(3i/)1/4, whereas for i 1, the scaling is x(i) = (9i/4)1/3. Substituting thesescalings into Eq. (113), and neglecting insignicant terms there, we obtainQ(i, ) _

_34_3

_14

34i if i c,49

2i if i c,(115)where the crossover degree value c = 1.66 is given by equating the two approximations above. Figure 10 shows theexact numerical solution for the in-degree distribution at t = , and juxtaposes it against these two approximations.The match is remarkable.We conclude that in-degree distribution in the universe causet at long times converges to the time-independentdistribution, which is a power law with exponent = 3/4 for small degrees ki [1, ca4], whereas for large degreeski > ca4, it is a power law with exponent = 2.2310-1610-1210-810-410010410810121016

i=ki /a410-3010-2010-1010010101020Q(

i,

)=

a4P(ki

,

)Exact numerical solutionApproximation for i<<cApproximation for i>>cFIG. 10: Exact numerical solution versus approximations for the in-degree distribution in the universe causet at the futureinnity. The solid line shows the numeric solution in Eqs. (113,114), while the dashed(-dotted) lines are the two approximationsin Eq. (115) for i c and i c. The crossover degree value c is shown by the dotted vertical line.G. Numeric evaluation of the analytic solutionGiven the results in the previous sections, the precise steps to numerically evaluate the analytic solution for thein-degree distribution in the universe at any given time are:1. For a given rescaled age of the universe , e.g. = 0, compute the current value of the rescaled scale factorr() = sinh2332, (116)and the normalization factorN() = 16 (sinh 3 3) . (117)2. According to Eq. (91), dene function(r) = 2

r 2F1_16, 12, 76, r3_, (118)where 2F1 is the hypergeometric function.3. Generate a (log-spaced) sequence of rescaled in-degrees i, and for each value of i in the sequence, ndnumerically the solution r

1 + x3. (120)5. Finally, according to Eq. (70), the value of the rescaled in-degree distribution at i is given byQ(i, ) = [r

(i)]4N()(i, r()). (121)24H. Universality of = 3/4 scaling for small degreesThe portion of the degree distribution with exponent = 3/4 for small degrees below a4remaining in theuniverse causet even at long times may appear as a paradox at the rst glance. Indeed, at long times, the universe isin its accelerating era dominated by dark energy, and the scale factor grows exponentially, versus polynomial growthin the matter-dominated era. Yet the degree distribution for small degrees behaves exactly the same way as in amatter-dominated universe, i.e. it has the same exponent = 3/4.The intuitive explanation of this paradox is as follows [41]. The degree distribution in the range of small degreesis shaped by spacetime quanta born at times

near the current time . The future horizon radii of these nodes aresmaller than the Hubble radius 1/H0 a. Therefore these nodes do not yet feel the acceleration of the universe.For them the universe expands as if it was matter-dominated.To formalize this intuition we check analytically that exponent = 3/4 for degrees ki a4is universal for anyscale factor. Keeping only the rst term in the Taylor expansion of

_1/4R3()4N()k3/4i , (123)The degree distribution for small degrees is thus a power law with universal exponent = 3/4, while the scale factoris relegated to normalization. The scale factor is important only for old spacetime quanta with large degrees, whereits exponential growth in asymptotically de Sitter spacetimes is responsible for the emergence of exponent = 2.25IV. RELATED WORKThe idea of replacing continuum spacetime with a graph or network appears in many approaches to quantumgravity, as it is a natural way to describe a discrete geometry. Causal Dynamical Triangulations (CDTs), for example,are formulated in terms of a simplicial triangulation of spacetime, which can be regarded as a (xed valence) graphin which adjacent simplices are connected by an edge [42, 43]. Another popular approach to quantum gravity is LoopQuantum Gravity, where the quantum states of geometry are described naturally in terms of spin networks, whichare graphs embedded into a three dimensional manifold. The edges and vertices in these graphs are colored withvarious mathematical structures, see [44] and references therein. There are a multitude of other approaches whosemathematical formulations have a similar discrete network-like character, such as Wolframs evolving networks [45],quantum graphity [46], DArianos causal networks [47], Requardts lumpy networks [48], etc.Most descriptions of quantum spacetime geometry in terms of a graph structure are of a purely spatial character,in that the description of spacetime arises from some form of temporal evolution of the graph whose edges usuallyconnect spatially nearest neighbors. The intuition behind this idea is clear, being similar to the idea of approximatinga continuous space by a ne lattice embedded in it. However, the precise physical meaning of the time in whichthe network evolves, and the manner in which the Lorentz invariant nature of special relativity can emerge from suchdiscreteness, are often unclear [11]. Causal sets and CDTs, however, are dierent in that they are described in termsof a graph-like structure which has a fundamentally spacetime character.The degree of a node is an eective measure of its age in growing complex networks. This simple observation ledus here to establishing a connection between these networks and the causal networks of discrete spacetime. As a nalnote, the notion of degree as a measure of time is reminiscent of unimodular gravity [49].AcknowledgmentsWe thank M.